From jeremylongshore-claude-code-plugins-plus-skills
Builds and configures scikit-learn pipelines for ML training, including data preparation, model training, hyperparameter tuning, and experiment tracking.
How this skill is triggered — by the user, by Claude, or both
Slash command
/jeremylongshore-claude-code-plugins-plus-skills:sklearn-pipeline-builderThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides automated assistance for sklearn pipeline builder tasks within the ML Training domain.
This skill provides automated assistance for sklearn pipeline builder tasks within the ML Training domain.
This skill activates automatically when you:
Example: Basic Usage Request: "Help me with sklearn pipeline builder" Result: Provides step-by-step guidance and generates appropriate configurations
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Part of the ML Training skill category. Tags: ml, training, pytorch, tensorflow, sklearn
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin ejentum-reasoningProvides scikit-learn API patterns for preprocessing, pipelines, model selection, evaluation, and hyperparameter tuning. Useful when /ds:experiment builds sklearn pipelines or evaluates models.
Guides ML workflows with scikit-learn: classification, regression, clustering, dimensionality reduction, preprocessing, pipelines, model evaluation, and hyperparameter tuning.
Builds end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.